1. Field of the Invention
This invention relates to systems and methodologies for implementing fault analysis systems, and particularly to systems and methods for determining correlation anomalies of specified variables.
2. Description of Background
Before our invention popular approaches to the performance of correlation analysis usually comprised the utilization of a correlation coefficient or, equivalently, a covariance matrix. In the event that two covariance matrices were calculated from differently tagged data sets, the problem of correlation analysis was reduced to the comparison between two p-dimensional normal distributions, where p is the number of variables involved. Conventionally, several known methods exist to determine whether or not two covariance matrices are statistically identical. However, these methods are applicable only to very static data. These methods, when used for data that takes into consideration the conditions of experimental fluctuations, the implementation of heterogeneities variables, and the diagnostic correlation between variables, an result in misleading answers.
In addition, if it has been determined that there is a significant difference between the two covariance matrices in one way or another, then the difference itself will not provide enough data for practical use. Because the information that is needed to facilitate a sufficient failure analysis determination is the information pertaining to which variable is abnormal. The use of this abnormal variable information is an essential element for an analysis tool to utilize in determining a course action that is to be taken in regard to a subject matter. If such actionable information is not provided, the analysis tool will be useless. For similar reasons, methodologies with which to calculate and visualize, or rank the difference between a normal correlation matrix and an abnormal correlation, have usability problems. Thus, traditional correlation coefficient analysis tools do not provide sufficient solutions.
Therefore, there exists a need for a method to identify anomalies for variables that have been calculated from correlation matrices that are based upon information that pertains to respective pairings of variables.